


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import ConditionalDetrImageProcessor







class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case=7   ,    snake_case=3   ,    snake_case=30   ,    snake_case=400   ,    snake_case=True   ,    snake_case=None   ,    snake_case=True   ,    snake_case=[0.5, 0.5, 0.5]   ,    snake_case=[0.5, 0.5, 0.5]   ,    snake_case=True   ,    snake_case=1 / 255   ,    snake_case=True   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
						UpperCamelCase__       =							parent
						UpperCamelCase__       =							batch_size
						UpperCamelCase__       =							num_channels
						UpperCamelCase__       =							min_resolution
						UpperCamelCase__       =							max_resolution
						UpperCamelCase__       =							do_resize
						UpperCamelCase__       =							size
						UpperCamelCase__       =							do_normalize
						UpperCamelCase__       =							image_mean
						UpperCamelCase__       =							image_std
						UpperCamelCase__       =							do_rescale
						UpperCamelCase__       =							rescale_factor
						UpperCamelCase__       =							do_pad




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						}







			def 					snake_case__					(				self   ,    snake_case   ,    snake_case=False  ):

						'''simple docstring'''


						if not batched:
									UpperCamelCase__       =							image_inputs[0]
									if isinstance(snake_case   ,    Image.Image  ):
												UpperCamelCase__,   UpperCamelCase__       =							image.size
									else:
												UpperCamelCase__,   UpperCamelCase__       =							image.shape[1], image.shape[2]
									if w < h:
												UpperCamelCase__       =							int(self.size["shortest_edge"] * h / w  )
												UpperCamelCase__       =							self.size["shortest_edge"]
									elif w > h:
												UpperCamelCase__       =							self.size["shortest_edge"]
												UpperCamelCase__       =							int(self.size["shortest_edge"] * w / h  )
									else:
												UpperCamelCase__       =							self.size["shortest_edge"]
												UpperCamelCase__       =							self.size["shortest_edge"]

						else:
									UpperCamelCase__       =							[]
									for image in image_inputs:
												UpperCamelCase__,   UpperCamelCase__       =							self.get_expected_values([image]  )
												expected_values.append((expected_height, expected_width)  )
									UpperCamelCase__       =							max(snake_case   ,    key=lambda snake_case  : item[0]  )[0]
									UpperCamelCase__       =							max(snake_case   ,    key=lambda snake_case  : item[1]  )[1]

						return expected_height, expected_width







@require_torch
@require_vision
class 							lowerCamelCase__						(				UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Optional[Any] 			=		ConditionalDetrImageProcessor if is_vision_available() else None




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							ConditionalDetrImageProcessingTester(self  )




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(snake_case   ,    "image_mean"  )  )
						self.assertTrue(hasattr(snake_case   ,    "image_std"  )  )
						self.assertTrue(hasattr(snake_case   ,    "do_normalize"  )  )
						self.assertTrue(hasattr(snake_case   ,    "do_resize"  )  )
						self.assertTrue(hasattr(snake_case   ,    "size"  )  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.image_processing_class.from_dict(self.image_processor_dict  )
						self.assertEqual(image_processor.size   ,    {"shortest_edge": 18, "longest_edge": 1333}  )
						self.assertEqual(image_processor.do_pad   ,    snake_case  )

						UpperCamelCase__       =							self.image_processing_class.from_dict(
						    self.image_processor_dict   ,    size=42   ,    max_size=84   ,    pad_and_return_pixel_mask=snake_case  )
						self.assertEqual(image_processor.size   ,    {"shortest_edge": 42, "longest_edge": 84}  )
						self.assertEqual(image_processor.do_pad   ,    snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						pass




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.image_processing_class(**self.image_processor_dict  )
						# create random PIL images
						UpperCamelCase__       =							prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=snake_case  )
						for image in image_inputs:
									self.assertIsInstance(snake_case   ,    Image.Image  )

						# Test not batched input
						UpperCamelCase__       =							image_processing(image_inputs[0]   ,    return_tensors="pt"  ).pixel_values

						UpperCamelCase__,   UpperCamelCase__       =							self.image_processor_tester.get_expected_values(snake_case  )

						self.assertEqual(
						    encoded_images.shape   ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,    )

						# Test batched
						UpperCamelCase__,   UpperCamelCase__       =							self.image_processor_tester.get_expected_values(snake_case   ,    batched=snake_case  )

						UpperCamelCase__       =							image_processing(snake_case   ,    return_tensors="pt"  ).pixel_values
						self.assertEqual(
						    encoded_images.shape   ,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )   ,    )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.image_processing_class(**self.image_processor_dict  )
						# create random numpy tensors
						UpperCamelCase__       =							prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=snake_case   ,    numpify=snake_case  )
						for image in image_inputs:
									self.assertIsInstance(snake_case   ,    np.ndarray  )

						# Test not batched input
						UpperCamelCase__       =							image_processing(image_inputs[0]   ,    return_tensors="pt"  ).pixel_values

						UpperCamelCase__,   UpperCamelCase__       =							self.image_processor_tester.get_expected_values(snake_case  )

						self.assertEqual(
						    encoded_images.shape   ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,    )

						# Test batched
						UpperCamelCase__       =							image_processing(snake_case   ,    return_tensors="pt"  ).pixel_values

						UpperCamelCase__,   UpperCamelCase__       =							self.image_processor_tester.get_expected_values(snake_case   ,    batched=snake_case  )

						self.assertEqual(
						    encoded_images.shape   ,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )   ,    )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.image_processing_class(**self.image_processor_dict  )
						# create random PyTorch tensors
						UpperCamelCase__       =							prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=snake_case   ,    torchify=snake_case  )
						for image in image_inputs:
									self.assertIsInstance(snake_case   ,    torch.Tensor  )

						# Test not batched input
						UpperCamelCase__       =							image_processing(image_inputs[0]   ,    return_tensors="pt"  ).pixel_values

						UpperCamelCase__,   UpperCamelCase__       =							self.image_processor_tester.get_expected_values(snake_case  )

						self.assertEqual(
						    encoded_images.shape   ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,    )

						# Test batched
						UpperCamelCase__       =							image_processing(snake_case   ,    return_tensors="pt"  ).pixel_values

						UpperCamelCase__,   UpperCamelCase__       =							self.image_processor_tester.get_expected_values(snake_case   ,    batched=snake_case  )

						self.assertEqual(
						    encoded_images.shape   ,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )   ,    )




			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
						with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"   ,    "r"  ) as f:
									UpperCamelCase__       =							json.loads(f.read()  )

						UpperCamelCase__       =							{"image_id": 39769, "annotations": target}

						# encode them
						UpperCamelCase__       =							ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"  )
						UpperCamelCase__       =							image_processing(images=snake_case   ,    annotations=snake_case   ,    return_tensors="pt"  )

						# verify pixel values
						UpperCamelCase__       =							torch.Size([1, 3, 800, 1066]  )
						self.assertEqual(encoding["pixel_values"].shape   ,    snake_case  )

						UpperCamelCase__       =							torch.tensor([0.2796, 0.3138, 0.3481]  )
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]   ,    snake_case   ,    atol=1E-4  )  )

						# verify area
						UpperCamelCase__       =							torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]  )
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]   ,    snake_case  )  )
						# verify boxes
						UpperCamelCase__       =							torch.Size([6, 4]  )
						self.assertEqual(encoding["labels"][0]["boxes"].shape   ,    snake_case  )
						UpperCamelCase__       =							torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]  )
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   ,    snake_case   ,    atol=1E-3  )  )
						# verify image_id
						UpperCamelCase__       =							torch.tensor([39769]  )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   ,    snake_case  )  )
						# verify is_crowd
						UpperCamelCase__       =							torch.tensor([0, 0, 0, 0, 0, 0]  )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]   ,    snake_case  )  )
						# verify class_labels
						UpperCamelCase__       =							torch.tensor([75, 75, 63, 65, 17, 17]  )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   ,    snake_case  )  )
						# verify orig_size
						UpperCamelCase__       =							torch.tensor([480, 640]  )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   ,    snake_case  )  )
						# verify size
						UpperCamelCase__       =							torch.tensor([800, 1066]  )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   ,    snake_case  )  )







			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
						with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"   ,    "r"  ) as f:
									UpperCamelCase__       =							json.loads(f.read()  )

						UpperCamelCase__       =							{"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

						UpperCamelCase__       =							pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"  )

						# encode them
						UpperCamelCase__       =							ConditionalDetrImageProcessor(format="coco_panoptic"  )
						UpperCamelCase__       =							image_processing(images=snake_case   ,    annotations=snake_case   ,    masks_path=snake_case   ,    return_tensors="pt"  )

						# verify pixel values
						UpperCamelCase__       =							torch.Size([1, 3, 800, 1066]  )
						self.assertEqual(encoding["pixel_values"].shape   ,    snake_case  )

						UpperCamelCase__       =							torch.tensor([0.2796, 0.3138, 0.3481]  )
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]   ,    snake_case   ,    atol=1E-4  )  )

						# verify area
						UpperCamelCase__       =							torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]  )
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]   ,    snake_case  )  )
						# verify boxes
						UpperCamelCase__       =							torch.Size([6, 4]  )
						self.assertEqual(encoding["labels"][0]["boxes"].shape   ,    snake_case  )
						UpperCamelCase__       =							torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]  )
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   ,    snake_case   ,    atol=1E-3  )  )
						# verify image_id
						UpperCamelCase__       =							torch.tensor([39769]  )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   ,    snake_case  )  )
						# verify is_crowd
						UpperCamelCase__       =							torch.tensor([0, 0, 0, 0, 0, 0]  )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]   ,    snake_case  )  )
						# verify class_labels
						UpperCamelCase__       =							torch.tensor([17, 17, 63, 75, 75, 93]  )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   ,    snake_case  )  )
						# verify masks
						UpperCamelCase__       =							822873
						self.assertEqual(encoding["labels"][0]["masks"].sum().item()   ,    snake_case  )
						# verify orig_size
						UpperCamelCase__       =							torch.tensor([480, 640]  )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   ,    snake_case  )  )
						# verify size
						UpperCamelCase__       =							torch.tensor([800, 1066]  )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   ,    snake_case  )  )






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase     		=							{
    'configuration_xlm_roberta_xl': [
        'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaXLConfig',
        'XLMRobertaXLOnnxConfig',
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase     		=							[
						    'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'XLMRobertaXLForCausalLM',
						    'XLMRobertaXLForMaskedLM',
						    'XLMRobertaXLForMultipleChoice',
						    'XLMRobertaXLForQuestionAnswering',
						    'XLMRobertaXLForSequenceClassification',
						    'XLMRobertaXLForTokenClassification',
						    'XLMRobertaXLModel',
						    'XLMRobertaXLPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    XLMRobertaXLConfig,
						    XLMRobertaXLOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm_roberta_xl import (
												    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMRobertaXLForCausalLM,
												    XLMRobertaXLForMaskedLM,
												    XLMRobertaXLForMultipleChoice,
												    XLMRobertaXLForQuestionAnswering,
												    XLMRobertaXLForSequenceClassification,
												    XLMRobertaXLForTokenClassification,
												    XLMRobertaXLModel,
												    XLMRobertaXLPreTrainedModel,
												)

else:
						import sys

						__UpperCamelCase     		=							_LazyModule(__name__, globals()['__file__'], _import_structure)


